Determination and correction of frequency registration deviations for quantitative spectroscopy

ABSTRACT

A frequency registration deviation is quantified for a field spectrum collected during analysis by a spectroscopic analysis system of a sample fluid when the spectroscopic analysis system has deviated from a standard calibration state. The field spectrum is corrected based on the frequency registration deviation using at least one spectral shift technique, and a concentration is calculated for at least one analyte represented by the field spectrum using the corrected field spectrum. Related systems, methods, and articles are described.

TECHNICAL FIELD

The subject matter described herein relates to spectroscopic analysis ingeneral, and more specifically to approaches for achieving andmaintaining accurate and reproducible frequency and/or wavelengthregistration of absorbance spectral data from a spectroscopic analyzer.

BACKGROUND

One or more of degradation, drift, or non-reproducibility of hardware ofa spectroscopic analysis system can affect frequency and wavelengthregistration and therefore the accuracy and reproducibility ofmeasurements made using such a system. These effects are generallyinevitable in real-world applications of spectroscopic analysis.Hardware of a spectroscopic analysis system can include light sources(e.g. lamps, lasers, or the like), electronics, optics, mechanicalcomponents, and the like. Achieving and maintaining accurate andreproducible frequency and wavelength registration of absorbancespectral data can be an important consideration in quantitativespectroscopy.

Currently available approaches to addressing these issues have includedreference cell technologies that use in-line or split beam pathconfigurations, periodic checks of frequency and/or wavelengthregistration using validation gas or gas mixtures, and peak tracking ofone or more strong spectral peaks of a target analyte and/or another“background” compound present in a sample fluid to correct for frequencyregistration deviations. However, reference or validation cells (e.g.,as described in co-owned U.S. Pat. No. 8,358,417, which is incorporatedherein by reference) can require additional hardware installation andpotentially add complexity to analytical system design. Periodicfrequency or wavelength registration checks using one or more standardgases or gas mixtures generally require switching mechanisms for a fluid(e.g. gas or liquid) containing a known concentration of the targetanalyte or another compound (which may or may not be present in theprocess sample fluid) that absorbs light in the target wavelengthregion, in addition to a supply of the consumable standardized fluid.This approach also interrupts continuous process measurements, which canlead to significant measurement blind time while performing systemvalidations. Peak tracking approaches can be susceptible to backgroundfluid composition changes as well as temperature and pressure effects.Additionally, while peak tracking generally can be used to correctlinear frequency registration deviation, it typically provides fewerbenefits in correcting for non-linear frequency registration deviation.

SUMMARY

In one aspect, a method includes quantifying a frequency registrationdeviation for a field spectrum collected during analysis by aspectroscopic analysis system of a sample fluid when the spectroscopicanalysis system has deviated from a standard calibration state. Thefield spectrum is corrected based on the frequency registrationdeviation using at least one spectral shift technique, and aconcentration is calculated for at least one analyte represented by thefield spectrum. The calculating includes applying the set of calibrationalgorithms to the corrected field spectrum.

In one aspect, a method includes quantifying a frequency registrationdeviation for a field spectrum collected during analysis by aspectroscopic analysis system of a sample fluid when the spectroscopicanalysis system has deviated from a standard calibration state,correcting the field spectrum based on the frequency registrationdeviation using at least one spectral shift technique, and calculating aconcentration for at least one analyte represented by the field spectrumusing the corrected field spectrum.

In optional variations, one or more of the following features can beincluded in any feasible combination. For example, in someimplementations the spectroscopic analysis system can optionally includeat least one of a laser light source and a non-laser light sourcedisposed to cause a beam of light to pass through the sample fluid atleast once, and a detector to quantify the field spectrum. In animplementation in which the spectroscopic analysis system includes thelaser light source, the laser light source can optionally include one ormore of a semiconductor laser, a tunable diode laser, a quantum cascadelaser, an intraband cascade laser, a horizontal cavity emitting laser, avertical cavity surface emitting semiconductor laser, a distributedfeedback laser, a distributed Bragg reflector laser, an external cavitytuned semiconductor laser, a gas discharge laser, a liquid laser, and asolid laser. In an implementation in which the spectroscopic analysissystem includes the non-laser light source, the non-laser light sourcecan optionally include one or more of a light emitting diode, anincandescent source, a thermal source, a discharge source, a laserassisted source, a laser driven plasma source, a fluorescent source, asuper-luminescent source, an amplified spontaneous emission (ASE)source, a super-continuum source, a spectrally broad source, and awidely tunable QCL source with a tunable grating type waveguide filter.

The spectroscopic analysis system can optionally further include asample cell to contain the sample fluid while the beam of light passesthrough the sample fluid at least once. Alternatively, the spectroscopicanalysis system can optionally further include a free space volume inwhich the sample fluid is positioned while a beam of light passesthrough the sample fluid at least once. The quantifying of the frequencyregistration deviation for the field spectrum can optionally includeapplying a set of calibration algorithms to the field spectrum, and/orthe quantifying of the frequency registration deviation for the fieldspectrum can optionally include using at least one frequencyregistration deviation function included in a set of calibrationalgorithms.

The set of calibration algorithms can optionally include a concentrationfunction for the spectroscopic analysis system, and the quantifying canoptionally include mathematically altering a frequency registrationdeviation of the field spectrum to create a predetermined number ofvariations, calculating one or more confidence indicators for eachvariation of the field spectrum after applying the concentrationfunction to all variations of the field spectrum, and modeling eachconfidence indicator or a combination of more than one confidenceindicator as a single-variate function of frequency registrationdeviation to mathematically determine an optimum frequency registrationdeviation that minimizes or maximizes the confidence indicator orcombination of more than one confidence indicator. The concentrationfunction can optionally be based on an unmodified calibration spectraldata set that does not include artificially generated frequencyregistration deviation spectra. The calculating of the concentration forthe analyte can optionally include applying the concentration functionto the field spectrum variation that corresponds to the optimumfrequency registration deviation.

The set of calibration algorithms can optionally include an output of acalculation engine based on multivariate analysis of a set ofcalibration data representative of the standard calibration state of thespectroscopic analysis system. The set of calibration data canoptionally include artificially generated frequency registrationdeviation spectra generated at design time by applying mathematicalshifts to calibration spectra collected using calibration samples. Thequantifying of the frequency registration deviation for the fieldspectrum can optionally include applying the set of calibrationalgorithms to calculate a characteristic indicator of frequencyregistration for the field spectrum and quantifying the frequencyregistration deviation for the field spectrum by comparing thecharacteristic indicator with a measured indicator of frequencyregistration determined from the field spectrum. The measured indicatorof frequency registration can optionally include one or more spectralfeatures and/or a spacing between the one or more spectral features.

The correcting can optionally include correcting the field spectrumbased on a quantified measurement state frequency registration deviationusing the at least one spectral shift technique. The at least onespectral shift technique can optionally include at least one of a linearshift, a nonlinear shift, a stretch of the measured spectrum, and acompression of the measured spectrum. The at least one spectral shifttechnique can optionally be applied in one or more of a purelymathematical manner, via hardware tuning, and by using a combination ofmathematical corrections and hardware tuning. The at least one spectralshift technique can optionally be applied to the entire field spectrumor to one or more individual sections of the field spectrum.

Systems and methods consistent with this approach are described as wellas articles that comprise a tangibly embodied machine-readable mediumoperable to cause one or more machines (e.g., computers, etc.) to resultin operations described herein. Similarly, computer systems are alsodescribed that may include computer hardware, such as for example one ormore processors and a memory coupled to the one or more processors. Thememory may include one or more programs that cause the one or moreprocessors to perform one or more of the operations described herein.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, show certain aspects of the subject matterdisclosed herein and, together with the description, help explain someof the principles associated with the disclosed implementations. In thedrawings,

FIG. 1 shows a diagram illustrating an example of a spectroscopicmeasurement system;

FIG. 2 shows a process flow diagram illustrating method featuresconsistent with implementations of the current subject matter;

FIG. 3 shows another process flow diagram illustrating method featuresconsistent with implementations of the current subject matter;

FIG. 4 shows another process flow diagram illustrating method featuresconsistent with implementations of the current subject matter;

FIG. 5 shows a diagram illustrating features relating to generation of aset of calibration algorithms consistent with implementations of thecurrent subject matter;

FIG. 6 shows another process flow diagram illustrating method featuresconsistent with implementations of the current subject matter;

FIG. 7 shows a diagram illustrating features relating to generation of aset of calibration algorithms consistent with implementations of thecurrent subject matter;

FIG. 8 shows a diagram illustrating features relating to generation of aset of calibration algorithms consistent with implementations of thecurrent subject matter;

FIG. 9 shows another process flow diagram illustrating method featuresconsistent with implementations of the current subject matter;

FIG. 10 shows a diagram illustrating features relating to generation ofconcentration model consistent with implementations of the currentsubject matter;

FIG. 11 shows a chart illustrating an example of mathematically alteringa sample spectrum to create a predetermined number of variationsconsistent with implementations of the current subject matter; and

FIG. 12 shows a chart illustrating an example of determining an optimumconfidence indicator consistent with implementations of the currentsubject matter.

When practical, similar reference numbers denote similar structures,features, or elements.

DETAILED DESCRIPTION

Consistent with implementations of the current subject matter,multivariate analysis approaches as described herein can be used tomodel and correct for frequency registration deviation effects that canoccur in a spectroscopic analysis system, without requiring changes tothe existing hardware of the spectroscopic analysis system, periodic (ornon-periodic) instrument validations using standard gases, or relianceon peak tracking of compounds in a fluid sample. In this manner, robust,reliable, and reproducible measurements can be achieved and maintained.

While example implementations of the current subject matter aredescribed herein in relation to harmonic spectroscopy techniques using awavelength or frequency modulated tunable diode laser absorbancespectrometer (TDLAS) or tunable semiconductor laser spectrometer, itwill be understood that approaches consistent with the current subjectmatter can be used in conjunction with analytical instrumentation ormethods relating to any quantitative spectroscopic approach, includingbut not limited to absorption, emission and fluorescence spectroscopy,such as, for example, Fourier transform infrared (FTIR) spectroscopy,non-dispersive infrared (NDIR) spectroscopy, cavity enhancedspectroscopy (CES), cavity ring-down spectroscopy (CRD), integratedcavity output spectroscopy (ICOS), photoacoustic spectroscopy, Ramanspectroscopy, and the like.

FIG. 1 shows a diagram of an example spectroscopic analysis system 100,which includes features that may appear in other spectroscopic analysissystems consistent with implementations of the current subject matter.The spectroscopic analysis system 100 can include a light source 102operating at one or more target wavelengths or over a range ofwavelengths. The light source 102 provides a continuous beam or pulsesof radiation (e.g. light in the visible, ultraviolet, infrared, or thelike, or other types of electromagnetic radiation) projected along alight path 104 that passes through a volume 106 of a sample fluid beforebeing detected by a detector 110. The light source 102 can optionallyinclude one or more lasers, for example a semiconductor laser, a tunablediode laser (TDL), a quantum cascade laser (QCL), an intraband cascadelaser (ICL), a horizontal cavity emitting laser (HCSEL), a verticalcavity surface emitting semiconductor laser (VCSEL), a distributedfeedback laser (DFB), a distributed Bragg reflector laser (DBR), anexternal cavity tuned semiconductor laser, a gas discharge laser, aliquid laser, a solid laser, and the like. The light source 102 can alsoor alternatively include one or more non-laser light sources, such asfor example a light emitting diode (LED), a lamp, and/or another devicecapable of generating frequency tunable light through nonlinear opticalinteractions and/or through spectral filtering. Examples of lamps caninclude, but are not limited to thermal sources, discharge sources,laser assisted or laser driven plasma sources, fluorescent sources,super-luminescent sources, amplified spontaneous emission (ASE) sources,super-continuum sources, and spectrally broad sources. Also includedwithin the scope of the current disclosure are examples such as widelytunable QCL sources with tunable grating type waveguide filters (e.g.those available from Redshift Systems of Burlington, Mass.).

The detector 110 can include one or more of a photodiode, aphotodetector, a photoacoustic detector, or other devices or structuresfor detecting an intensity of the radiation emitted by the light source102 after the path has passed at least once through the volume 106. Insome implementations, the volume 106 can be contained in a sample cell112 having one or more windows or other openings 114 through which thelight path 104 passes into and out of the volume 106. The sample cell112 can be a flow through cell as shown in FIG. 1, in which fluid flowsinto the sample cell 112 via an inlet 116 and out of the sample cell 112through an outlet 120. In still other implementations of the currentsubject matter, an analytical system can omit a sample cell and caninstead be configured such that the light path passes at least oncethrough an open (e.g. unbounded or unenclosed) space (e.g. within astack, in the open atmosphere, etc.) in traversing between the lightsource 102 and the detector 110. In an open path system consistent withthis implementation, the light path can optionally include one or morereflections via mirrors or other reflective surfaces arranged within oradjacent to the open space volume.

Other configurations are possible besides that shown in FIG. 1. Forexample, a path length of the light path 104, which is the distance thecontinuous beam or pulses of radiation travels through the sample fluid,can be established using mirrors, beam splitters, or by varying othergeometrical parameters such as the location of the light source 102and/or the detector 110. Furthermore, the sample volume can contain anon-enclosed open path between the light source 102 and the detector110. Depending on the analyte or analytes to be measured, theconcentration range over which one or more analytes are expected to bepresent, and the presence of other compounds or materials that mightinterfere with the accuracy of a measurement in the sample, thecontinuous beam or pulses of light can be projected through free fluid(such as for example in a pipeline, an exhaust stack, etc.) or even freeair or liquid (such as for example in the open atmosphere, a body ofwater, etc.). Alternatively, a batch volume 106 of sample fluid can beanalyzed in a sample cell 112, for example one such as that shown inFIG. 1 with additional conduits or tubing, valves, and/or vacuum orpumping equipment to deliver a first batch volume 106 and tosubsequently remove that first batch volume from the sample cell 112 toprepare for analysis of a second batch volume. A controller 122 can beincorporated to receive and analyze the detector data from the detector110, to control the light source 102, and optionally to perform one ormore of the operations discussed below in relation to virtualreconstruction of a calibration state of the spectroscopic analysissystem 100.

Modulation spectroscopy, which is also referred to as harmonicspectroscopy, is a widely used technique for sensitive detection ofanalyte(s) at very low concentrations (e.g. in the sub-parts-per-millionor sub-parts-per-billion range). In modulation spectroscopy, thewavelength and/or the amplitude of the light source 102 is modulated ata modulation frequency f. Light emitted by the laser light source 102 ispassed through the sample gas 106 over a path length. The intensity ofthe continuous beam or pulses of light 104 as it impinges on thedetector 110 can optionally vary in amplitude. Fourier analysis of thesignal generated by the detector 110 includes signal components at themodulation frequency f as well as at harmonic frequencies at multiplesof the modulation frequency f (e.g. 2f, 3f, 4f, etc.). Demodulation ofone of the harmonic frequencies, for example the 2f, yields a signalthat can be used to very accurately determine the concentration of oneor more analytes in the sample fluid 106. By shifting phase-sensitivedetection to higher frequencies, modulation spectroscopy cansignificantly reduce 1/f noise and achieve high sensitivity. Modulationspectroscopy can be highly sensitive for detecting and quantifying lowanalyte concentrations, and an analyte concentration can be quantifieddirectly from the demodulated signal from the detector 110.Additionally, a lock-in amplifier or other signal filtering processes ordevices can be used to isolate absorbance signals due to the analytefrom background drift or other noise in the instrument. Otherspectroscopic approaches can include one or more of these and optionallyother features or processes.

The term spectral data refers to data quantifying one or more of anabsorbance, a reflectance, a fluorescence, a scattering, or an emissionoccurring in response to incident light interacting with molecules of asample fluid such as a gas or a liquid in a spectroscopic analysissystem. Terms used in this disclosure in describing changes tospectroscopic analysis system performance that can occur as a result ofhardware variations over time include frequency registration (FR), whichrefers to an alignment of a frequency axis (commonly the x-axis) ofspectral data; frequency registration deviation (FRD), which refersgenerally to any changes or deviations to the frequency axis of spectraldata obtained from a spectroscopic analysis system; and an indicator offrequency registration (IFR), which refers to one or more spectralfeatures (e.g. a peak, a valley, a zero crossing point, an inflectionpoint, or another characteristic point) in the spectral data, and/or thespacing between defined or chosen spectral features.

Spectral data refers to one or more sets of spectroscopic data collectedusing a spectroscopic analysis system. Field spectral data refers tospectroscopic data collected using a spectroscopic analysis system toanalyze one or more field samples, while calibration spectral datarefers to spectroscopic data collected using a spectroscopic analysissystem to analyze one or more calibration samples. Field sample is aterm used herein to refer to a fluid (e.g. gas or liquid) containing anunknown quantity of one or more analytes of interest, while acalibration sample is one for which one or more analyte concentrationsare known or well characterized. An analyte refers generally to anelement or a compound having one or more spectral features for which thespectroscopic analysis system is configured to capture spectral data. Aspectral measurement state refers to a state of the hardware of thespectroscopic analysis system at the time that spectral data arecollected.

A calibration state refers to a state of the hardware of thespectroscopic analysis system when the spectroscopic analysis system iscalibrated, for example when calibration spectral data are collected.Calibration spectral data refer to spectral data collected using aspectroscopic analysis system to analyze one or more calibration sampleshaving a known or well-characterized amount of an analyte or some otherelement or compound and optionally one or more other known orwell-characterized measurement parameters, such as for exampletemperature, pressure, a background composition of the calibrationsample, etc. A function, as used herein, refers to a mathematicaloperation or set of mathematical operations that result in atransformation of a set of data. An example of a function is a vector ormatrix that includes values that mathematically operate on a spectraldata set.

Regardless of the spectroscopy technique used, an approach consistentwith the current subject matter generally involves one or morecalibration models and/or calibration algorithms built in the factoryusing one or more sets of calibration spectral data. Such calibrationspectral data can be advantageously designed to be mathematicallyrepresentative of concentration ranges expected to be encountered forfield samples analyzed using the spectroscopic analysis system. Thequantitative characteristics of field samples, including but not limitedto one or more analyte concentrations (e.g. partial pressure, molefraction, mass or number of moles per volume, volume ratio, or thelike), sample pressure, sample temperature, flow rate, viscosity, etc.,can be calculated or predicted by applying the one or more calibrationmodels to measured field sample spectra. Unless otherwise specified orotherwise inconsistent with the context in which it appears, the termconcentration is used generically to refer to any of the possiblequantitative characteristics listed above. The frequency registrationdeviation of a spectral measurement state generated by a spectroscopicanalysis system can be quantified using approaches similar to thosedescribed herein. This quantified frequency registration deviation canbe used to correct a current spectral measurement state to best emulatean original calibration state of the system, based on which aconcentration can be calculated.

In some advantageous implementations of the current subject matter, auniversally-accepted standard calibration state for a spectroscopicanalysis system can be established and recorded. For example, thisstandard calibration state can be characterized at a factory or assemblyfacility at which the spectroscopic analysis system is manufactured, atesting facility, etc., and can be defined for multiple individual unitsof a factory-standard spectroscopic analysis system. A subsequentmeasurement state of any spectroscopic analysis system with the samerelevant technical specifications can be referenced to this standardcalibration state, thus tying together the calibration state of thesimilar spectroscopic analysis systems. The frequency registrationdeviation correction can be used on a single spectroscopic analysissystem operating in the field to correct its measurement state. Inaddition or alternatively, a frequency registration deviation correctioncan be used on a more universal level for correcting the calibrationstate of multiple comparable instances of a spectroscopic analysissystem to the standard calibration state.

Further advantages that can be realized with implementations of thecurrent subject matter can include real time measurement statecorrection that allows for a more robust system. Such measurement statecorrections can reduce or eliminate susceptibility to hardwaredegradation, drift and/or non-reproducibility, thereby assisting inmaintaining the fidelity of the quantitative measurements. Systemlifetime of instruments in the field can also be increased, and customerreturns reduced.

A calibration model such as those described herein can be built and thefrequency registration deviation can be quantified and corrected usingvarious multivariate analysis methods that include, but are not limitedto, classical least square regression (CLS), inverse least squareregression (ILS), principal component analysis (PCA), principalcomponent regression (PCR), partial least square regression (PLS),multiple linear regression (MLR), etc. The quantification can be appliedto an entire measured spectrum or to one or more individual sections ofthe measured spectrum as well as sample pressure and temperature data,or other relevant measurement data. These multivariate techniques can beapplied by a calculation engine embedded in the spectroscopic analysissystem or by standalone commercially available software, for exampleexecuting on one or more general purpose computers.

The correction of a frequency registration deviation for a spectroscopicanalysis system can include one or more spectral shift correctiontechniques, such as for example a linear shift, a nonlinear shift, astretch of the measured spectrum, a compression of the measuredspectrum, etc. These corrections can be applied either in a purelymathematical manner (e.g. by performing a mathematical transformation ona collected field sample spectrum), via hardware tuning (which canoptionally include but is not limited to, adjustments to one or more ofa laser operating temperature, a laser operating current (e.g. a nominalcurrent value and/or a current ramp rate), a laser modulation current, ademodulation phase, a detection phase, a modulation frequency, amodulation frequency, a detection gain, or the like), or using acombination of mathematical corrections and hardware tuning. Thecorrection can be applied to the entire or individual sections of ameasured field sample spectrum.

In general, calculations of analyte concentrations can be improved inaccordance with implementations of the current subject matter through aprocess in which a pre-determined calibration model for a spectroscopicanalysis system is used in quantifying a frequency registrationdeviation and applying a correction to a field spectrum. As discussed ingreater detail below, the calibration model can include a set ofcalibration algorithms, which can optionally be based on a calibrationdata set collected using the spectroscopic analysis system.Alternatively or in addition, the calibration model can include aconcentration function generated by a calculation engine using a nullcalibration set as input. FIG. 2 shows a process flow chart illustratingfeatures of a method consistent with the current subject matter. At 202,such a method includes use by the spectroscopic analysis system of thecalibration model to determine a correction to be applied to a fieldspectrum obtained using the spectroscopic analysis system. Thecorrection compensates or otherwise corrects for a frequencyregistration deviation that has occurred for the spectroscopic analysissystem relative to a previous calibration state. This correction is thenapplied at 204 to calculate a concentration of one or more analyteshaving spectral features captured in the field spectrum. The followingdescriptions of implementations of the current subject matter includedetails relating to one or more of these more general features.

FIG. 3 shows a process flow chart illustrating features of a method 300consistent with implementations of the current subject matter. At 302,the method 300 includes accessing the calibration model, which in thisexample can include a set of calibration algorithms for a spectroscopicanalysis system that are based on a multivariate analysis of acalibration spectral data set. The accessing of one or more calibrationmodels, algorithms, functions, etc. referred to in this disclosure cangenerally indicate that a processor or other computer hardware, whichcan optionally be part of a spectroscopic analysis system oralternatively a remote computing system or systems that is or areconfigured to exchange data with the spectroscopic analysis system, isprepared to execute the other processes discussed. In some examples, theaccessing can include reading the one or more calibration models,algorithms, functions, etc. from a local computer or machine-readablestorage device or alternatively can include receiving this informationover a network or other data connection from a remote system. It will beunderstood that accessing of the one or more calibration models,algorithms, functions, etc. is not essential as the processor or othercomputer hardware performing the various calculations and correctionscan be pre-loaded with the calibration models, algorithms, functions,etc., either at design time or at start-up, etc.

The calibration spectral data set can be collected by using thespectroscopic analysis system to collect spectral data for a variety ofknown conditions, which can, in some implementations of the currentsubject matter, be selected from varying concentrations of one or moreanalytes, varying pressure or temperature, varying the concentration ofone or more other compounds (besides the one or more target analytes),varying the laser operating current (e.g. the nominal current valueand/or the current ramp rate), varying the laser operating temperature,or the like.

The set of calibration algorithms can include one or more models, oralternatively one or more matrices (e.g. vectors, sets of vectors,etc.), functions, algorithms, statistical tools, or the like that can beused in conjunction with field spectral data to predict either or bothof an indicator of frequency registration and a frequency registrationdeviation of the spectroscopic analysis system at the time of collectionof the field spectral data and also to predict a concentration of one ormore analytes in the sample fluid for which the field spectrum wascollected. The set of calibration algorithms are applied at 304 to afield spectrum collected by the spectroscopic analysis system for asample fluid, thereby quantifying a frequency registration deviation forthe field spectrum. The quantified frequency registration deviation canbe used at 306 to correct the field spectrum using one or more spectralshifting techniques such as those discussed above. The method furtherincludes calculating a concentration for the one or more analytesrepresented by the field spectrum at 310.

It will be understood from FIG. 3 and the accompanying description inthe preceding paragraphs that the generating of the set of calibrationalgorithms can be a “design time” process, for example one that isperformed at a factory or assembly location for a given spectroscopicanalysis system. In some examples, the generation of the set ofcalibration algorithms can occur when a spectroscopic analysis system isfirst manufactured and before the spectroscopic analysis system isplaced into service. Alternatively or in addition, the set ofmathematical corrections can be generated as part of a recalibrationprocess that occurs either at a recalibration or refurbishment facility,at the factory, in the field, etc. Additionally, as noted above, the setof calibration algorithms can optionally represent a standardcalibration state of multiple physical instances of a givenconfiguration of the spectroscopic analysis system and can be determinedbased on a different physical spectroscopic analysis system than thespectroscopic analysis system for which it is used.

Various implementations of the current subject matter can includedifferent approaches for generating the set of mathematical correctionsand for the use of these mathematical corrections in predictingfrequency registration deviation for a given field spectral data setcollected by the spectroscopic analysis system. FIG. 4, FIG. 6, and FIG.9 show process flow charts 400, 600, 900 illustrating features ofmethods consistent with some example approaches based on the genericapproach explained above in reference to FIG. 2 and FIG. 3.

As shown in the process flow chart 400 of FIG. 4, in one approach, acomputing device used for processing spectral data generated by aspectroscopic analysis system can access a set of calibration algorithmsat 402. As noted previously, the processes leading to the creation ofthe set of calibration algorithms (discussed in further detail below inreference to FIG. 5) can be performed at design time and then loadedinto memory or other computer-readable storage accessible by aspectroscopic analysis system controller 122 or other processorassociated with a spectroscopic analysis system or otherwise receivingspectral data from a spectroscopic analysis system. As furtherillustrated in the diagram 500 of FIG. 5, the set of calibrationalgorithms 502 in this example are generated at design time using acalculation engine 504 based on inputs of an unmodified calibrationspectral data set 506. The unmodified calibration spectral data set 506can include a set of spectral data obtained through analysis of multiplesamples over a chosen set of variations in one or more variables such asconcentration, pressure, temperature, fluid flow rate, etc. Noartificial or mathematical spectral shifts need to be applied to theunmodified calibration spectral data set 506 in this implementation.

The calculation engine 504 generates the set of calibration algorithms502 optionally including either models or calibration functions relatingto an indicator of frequency registration 508 and concentration 510. Thecalculation engine 504 can generate the set of calibration algorithms502 based on a multivariate analysis of the unmodified calibrationspectral data set 506, for example using CLS, PCA, PLS, etc. Anindicator model for frequency registration can be used in predicting anindicator of frequency registration, and a concentration model can beused in calculating the concentration of one or more analytes in thefield spectra data. Alternatively, the calibration algorithms can bebased on a single model of the spectroscopic analysis system calibrationstate result. In such an approach, the calibration algorithms can beused to determine two calibration functions (e.g. calibration vectors,calibration matrices, etc.) for use in making frequency registrationdeviation predictions and concentration calculations, a first of whichcan be used in predicting an indicator of frequency registration forfield spectral data, and a second of which can be used in calculatingthe concentration of one or more analytes in the field spectra data.

At 404, a characteristic indicator of frequency registration of fieldspectrum data collected for a field sample is calculated by applying theset of calibration algorithms 502. For example, the indicator offrequency registration model or the indicator of frequency registrationcalibration function discussed in the preceding paragraph can be appliedto the field spectrum data. The characteristic indicator of frequencyregistration is compared with a measured indicator of frequencyregistration determined from the field spectrum to determine a frequencyregistration deviation for the spectroscopic analysis system at 406.This measured indicator of frequency registration can optionally be oneor more spectral features and/or a spacing between the one or morespectral features. At 410, the field spectrum is corrected based on thefrequency registration deviation using one or more spectral shiftcorrection techniques as discussed above, and at 412, the concentrationmodel is applied to the corrected field spectrum to calculate an analyteconcentration associated with that field spectrum.

As with methods consistent with FIG. 4, the method illustrated in theprocess flow chart 600 of FIG. 6 can include a computing device used forprocessing spectral data generated by a spectroscopic analysis systemaccessing a set of calibration algorithms at 602. As noted previously,the processes leading to the creation of the set of calibrationalgorithms 502 can optionally be performed at design time and thenloaded into memory or other computer-readable storage accessible by aspectroscopic analysis system controller 122 or other processorassociated with a spectroscopic analysis system or otherwise receivingspectral data from a spectroscopic analysis system. As furtherillustrated in the diagram 700 of FIG. 7, the set of calibrationalgorithms 502 in this example are generated at design time using acalculation engine 504 based on inputs of a calibration spectral dataset 702. In this example, however, the calibration spectral data set 702includes artificially generated frequency registration deviation spectraand optionally also unmodified “null” calibration spectral data. Theartificially generated frequency registration deviation spectra aregenerated by applying mathematical shifts to calibration spectracollected using calibration samples. The output of the calculationengine 504 is a model for the spectroscopic analysis system calibrationstate that can generate a set of calibration algorithms 502 includingboth of a frequency registration deviation prediction function 708 and aconcentration calculation function 710.

Referring again to FIG. 6, at 604 the frequency registration deviationfunction 708 can be used to quantify a measurement state frequencyregistration deviation of a field spectrum collected by a spectroscopicanalysis system. The field spectrum can be corrected at 606 based onthis quantified measurement state frequency registration deviation, forexample using one or more spectral shift correction techniques asdiscussed above. At 610, the concentration function 710 is applied tothe corrected field spectrum to calculate an analyte concentrationassociated with that field spectrum.

A method consistent with FIG. 6 can also be used in conjunction with afrequency registration deviation function 708 and a concentrationfunction 710 generated as illustrated in the diagram 800 of FIG. 8. Theset of calibration algorithms 502 in this example are generated atdesign time using a calibration engine 504 based on inputs of twocalibration spectral data sets 802, 804. A first calibration spectraldata set 802 includes an unmodified null calibration spectral data setthat does not include artificially generated frequency registrationdeviation spectra. A second calibration spectral data set 804 includesboth calibration spectra and artificially generated frequencyregistration deviation spectra generated by applying one or more ofmathematical shifts and hardware tuning (e.g. any of performing amathematical transformation on a collected calibration sample spectrum,adjusting a laser operating temperature, adjusting a laser operatingcurrent, adjusting a laser modulation current, adjusting a demodulationphase, adjusting a detection phase, either individually or in anycombination) to calibration spectra collected using calibration samples.In this example, parallel calculation models 504A, 504B, oralternatively, a single calculation engine (not shown in FIG. 8) doingserial processing of the calculations required, generate a set ofcalibration algorithms 502 including a frequency registration deviationfunction 708 based on the second calibration spectral data set 804 and aconcentration function 710 based on the first calibration spectral dataset 802.

The method illustrated in the process flow chart 900 of FIG. 9 involvesthe use of one or more confidence indicators. A confidence indicator isa statistical tool to describe how well a calibration model can coverone or more field-measured samples. In implementations of the currentsubject matter, a confidence indicator can be used to determine thenecessary measurement state change needed to best match the measurementstate to a stored representation of an actual calibration state.Examples of confidence indicator functions that can be used in thismanner include, but are not limited to, spectral residual, Mahalanobisdistance, a variance indicators (e.g., mean squared error, root meansquared error, R-squared, or the like), etc. At design time, aconcentration function (or concentration model) 710 is generated by acalculation engine 504 using as input a null calibration set 1002 asshown in the diagram 1000 of FIG. 10. The calibration function 710 canbe loaded into memory or other computer-readable storage accessible by aspectroscopic analysis system controller 122 or other processorassociated with a spectroscopic analysis system or otherwise receivingspectral data from a spectroscopic analysis system.

As illustrated in FIG. 9, at 902, the method can include a computingdevice used for processing spectral data generated by a spectroscopicanalysis system accessing a concentration model that includes one ormore calibration functions 710. At 904, a frequency registrationdeviation of a field sample spectrum is mathematically altered to createa predetermined number of variations as shown in the chart 1100 of FIG.11. These mathematical alterations can include linear or nonlinearfrequency shifts, stretches, compressions, etc. of the field spectrum.The concentration model 710 is applied to all of the predeterminednumber of variations of the field spectrum before one or more confidenceindicators are calculated for each of the variations of the fieldspectrum at 906. At 910, each confidence indicator or combination ofmore than one confidence indicator is modeled as a single-variatefunction of the frequency registration deviation such that an optimumfrequency registration deviation that either minimizes or maximizes theconfidence indicator or the combination of confidence indicators ismathematically determined as shown in the chart 1200 of FIG. 12. At 912,the concentration function 710 is applied to the particular fieldspectrum variation that corresponds to an optimum frequency registrationdeviation to calculate an analyte concentration associated with thatfield spectrum, as the particular variation corresponding to the optimumfrequency registration deviation is the variation that best matches theoriginal calibration state of the spectroscopic analysis system.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or codes,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including, but notlimited to, acoustic, speech, or tactile input. Other possible inputdevices include, but are not limited to, touch screens or othertouch-sensitive devices such as single or multi-point resistive orcapacitive trackpads, voice recognition hardware and software, opticalscanners, optical pointers, digital image capture devices and associatedinterpretation software, and the like. A computer remote from ananalyzer can be linked to the analyzer over a wired or wireless networkto enable data exchange between the analyzer and the remote computer(e.g. receiving data at the remote computer from the analyzer andtransmitting information such as calibration data, operating parameters,software upgrades or updates, and the like) as well as remote control,diagnostics, etc. of the analyzer.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it used, such a phrase is intendedto mean any of the listed elements or features individually or any ofthe recited elements or features in combination with any of the otherrecited elements or features. For example, the phrases “at least one ofA and B;” “one or more of A and B;” and “A and/or B” are each intendedto mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” Use of the term “based on,” above and in theclaims is intended to mean, “based at least in part on,” such that anunrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

1. A computer-implemented method comprising: quantifying a frequencyregistration deviation for a field spectrum collected during analysis bya spectroscopic analysis system of a sample fluid when the spectroscopicanalysis system has deviated from a standard calibration state;correcting the field spectrum based on the frequency registrationdeviation using at least one spectral shift technique; and calculating aconcentration for an analyte represented by the field spectrum using thecorrected field spectrum. 2.-37. (canceled)